Mathematical model predicts best way to build muscle

Date:
August 23, 2021
Source:
University of Cambridge
Summary:
Researchers have developed a mathematical model that can predict
the optimum exercise regime for building muscle.



FULL STORY ========================================================================== Researchers have developed a mathematical model that can predict the
optimum exercise regime for building muscle.


==========================================================================
The researchers, from the University of Cambridge, used methods of
theoretical biophysics to construct the model, which can tell how much
a specific amount of exertion will cause a muscle to grow and how long
it will take. The model could form the basis of a software product,
where users could optimise their exercise regimes by entering a few
details of their individual physiology.

The model is based on earlier work by the same team, which found that
a component of muscle called titin is responsible for generating the
chemical signals which affect muscle growth.

The results, reported in the Biophysical Journal, suggest that there is
an optimal weight at which to do resistance training for each person and
each muscle growth target. Muscles can only be near their maximal load
for a very short time, and it is the load integrated over time which
activates the cell signalling pathway that leads to synthesis of new
muscle proteins. But below a certain value, the load is insufficient
to cause much signalling, and exercise time would have to increase exponentially to compensate. The value of this critical load is likely
to depend on the particular physiology of the individual.

We all know that exercise builds muscle. Or do we? "Surprisingly, not
very much is known about why or how exercise builds muscles: there's
a lot of anecdotal knowledge and acquired wisdom, but very little in
the way of hard or proven data," said Professor Eugene Terentjev from Cambridge's Cavendish Laboratory, one of the paper's authors.

When exercising, the higher the load, the more repetitions or the greater
the frequency, then the greater the increase in muscle size. However,
even when looking at the whole muscle, why or how much this happens isn't known. The answers to both questions get even trickier as the focus goes
down to a single muscle or its individual fibres.



========================================================================== Muscles are made up of individual filaments, which are only 2 micrometres
long and less than a micrometre across, smaller than the size of the
muscle cell.

"Because of this, part of the explanation for muscle growth must be
at the molecular scale," said co-author Neil Ibata. "The interactions
between the main structural molecules in muscle were only pieced together around 50 years ago.

How the smaller, accessory proteins fit into the picture is still not
fully clear." This is because the data is very difficult to obtain:
people differ greatly in their physiology and behaviour, making it almost impossible to conduct a controlled experiment on muscle size changes in a
real person. "You can extract muscle cells and look at those individually,
but that then ignores other problems like oxygen and glucose levels during exercise," said Terentjev. "It's very hard to look at it all together." Terentjev and his colleagues started looking at the mechanisms of mechanosensing -- the ability of cells to sense mechanical cues in their environment -- several years ago. The research was noticed by the English Institute of Sport, who were interested in whether it might relate to
their observations in muscle rehabilitation. Together, they found that
muscle hyper/ atrophy was directly linked to the Cambridge work.

In 2018, the Cambridge researchers started a project on how the proteins
in muscle filaments change under force. They found that main muscle constituents, actin and myosin, lack binding sites for signalling
molecules, so it had to be the third-most abundant muscle component --
titin -- that was responsible for signalling the changes in applied force.

Whenever part of a molecule is under tension for a sufficiently long time,
it toggles into a different state, exposing a previously hidden region. If
this region can then bind to a small molecule involved in cell signalling,
it activates that molecule, generating a chemical signal chain. Titin
is a giant protein, a large part of which is extended when a muscle is stretched, but a small part of the molecule is also under tension during
muscle contraction.

This part of titin contains the so-called titin kinase domain, which is
the one that generates the chemical signal that affects muscle growth.



==========================================================================
The molecule will be more likely to open if it is under more force, or
when kept under the same force for longer. Both conditions will increase
the number of activated signalling molecules. These molecules then induce
the synthesis of more messenger RNA, leading to production of new muscle proteins, and the cross-section of the muscle cell increases.

This realisation led to the current work, started by Ibata, himself a
keen athlete. "I was excited to gain a better understanding of both the
why and how of muscle growth," he said. "So much time and resources could
be saved in avoiding low-productivity exercise regimes, and maximising athletes' potential with regular higher value sessions, given a specific
volume that the athlete is capable of achieving." Terentjev and Ibata
set out to constrict a mathematical model that could give quantitative predictions on muscle growth. They started with a simple model that kept
track of titin molecules opening under force and starting the signalling cascade. They used microscopy data to determine the force-dependent
probability that a titin kinase unit would open or close under force
and activate a signalling molecule.

They then made the model more complex by including additional information,
such as metabolic energy exchange, as well as repetition length and
recovery. The model was validated using past long-term studies on muscle hypertrophy.

"Our model offers a physiological basis for the idea that muscle growth
mainly occurs at 70% of the maximum load, which is the idea behind
resistance training," said Terentjev. "Below that, the opening rate of
titin kinase drops precipitously and precludes mechanosensitive signalling
from taking place.

Above that, rapid exhaustion prevents a good outcome, which our
model has quantitatively predicted." "One of the challenges in
preparing elite athletes is the common requirement for maximising
adaptations while balancing associated trade-offs like energy costs,"
said Fionn MacPartlin, Senior Strength & Conditioning Coach at the
English Institute of Sport. "This work gives us more insight into the
potential mechanisms of how muscles sense and respond to load, which can
help us more specifically design interventions to meet these goals."
The model also addresses the problem of muscle atrophy, which occurs
during long periods of bed rest or for astronauts in microgravity,
showing both how long can a muscle afford to remain inactive before
starting to deteriorate, and what the optimal recovery regime could be.

Eventually, the researchers hope to produce a user-friendly software-based application that could give individualised exercise regimes for specific
goals.

The researchers also hope to improve their model by extending their
analysis with detailed data for both men and women, as many exercise
studies are heavily biased towards male athletes.

========================================================================== Story Source: Materials provided by University_of_Cambridge. The original
text of this story is licensed under a Creative_Commons_License. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Neil Ibata, Eugene M. Terentjev. Why exercise builds muscles: titin
mechanosensing controls skeletal muscle growth under
load. Biophysical Journal, 2021; DOI: 10.1016/j.bpj.2021.07.023 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/08/210823125730.htm

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